Lack of consistency in measurement methods and semantics used for network measures in adolescent health behaviour studies using social network analysis: a systematic review

BACKGROUND

Social network analysis (SNA) is often used to examine how social relationships influence adolescent health behaviours, but no study has documented the range of network measures used to do so. We aimed to identify network measures used in studies on adolescent health behaviours.

METHODS

We conducted a systematic review to identify network measures in studies investigating adolescent health behaviours with SNA. Measures were grouped into eight categories based on network concepts commonly described in the literature: popularity, position within the network, network density, similarity, nature of relationships, peer behaviours, social norms, and selection and influence mechanisms. Different subcategories were further identified. We detailed all distinct measures and the labels used to name them in included articles.

RESULTS

Out of 6686 articles screened, 201 were included. The categories most frequently investigated were peer behaviours (n=201, 100%), position within the network (n=144, 71.6%) and popularity (n=110, 54.7%). The number of measurement methods varied from 1 for 'similarity on popularity' (within the 'similarity' category) to 28 for the 'characterisation of the relationship between the respondent and nominated peers' (within the 'nature of the relationships' category). Using the examples of 'social isolation', 'group membership', 'individuals in a central position' (within the 'position within the network' category) and 'nominations of influential peers' (sub within the 'popularity' category), we illustrated the inconsistent reporting and heterogeneity in measurement methods and semantics.

CONCLUSION

Robust methodological recommendations are needed to harmonise network measures in order to facilitate comparison across studies and optimise public health intervention based on SNA.

A Systematic Review of Qualitative Studies on Factors Associated With Smoking Cessation Among Adolescents and Young Adults

OBJECTIVE

To summarize findings from qualitative studies on factors associated with smoking cessation among adolescents and young adults.

DATA SOURCES

We searched Pubmed, Psychinfo, CINAHL, Embase, Web of Science, and SCOPUS databases, as well as reference lists, for peer-reviewed articles published in English or French between January 1, 2000, and November 18, 2020. We used keywords such as adolescents, determinants, cessation, smoking, and qualitative methods.

STUDY SELECTION

Of 1724 records identified, we included 39 articles that used qualitative or mixed methods, targeted adolescents and young adults aged 10-24, and aimed to identify factors associated with smoking cessation or smoking reduction.

DATA EXTRACTION

Two authors independently extracted the data using a standardized form. We assessed study quality using the National Institute for Health and Care Excellence checklist for qualitative studies.

DATA SYNTHESIS

We used an aggregative meta-synthesis approach and identified 39 conceptually distinct factors associated with smoking cessation. We grouped them into two categories: (1) environmental factors [tobacco control policies, pro-smoking norms, smoking cessation services and interventions, influence of friends and family], and (2) individual attributes (psychological characteristics, attitudes, pre-quitting smoking behavior, nicotine dependence symptoms, and other substances use). We developed a synthetic framework that captured the factors identified, the links that connect them, and their associations with smoking cessation.

CONCLUSIONS

This qualitative synthesis offers new insights on factors related to smoking cessation services, interventions, and attitudes about cessation (embarrassment when using cessation services) not reported in quantitative reviews, supplementing limited evidence for developing cessation programs for young persons who smoke.

IMPLICATIONS

Using an aggregative meta-synthesis approach, this study identified 39 conceptually distinct factors grouped into two categories: Environmental factors and individual attributes. These findings highlight the importance of considering both environmental and individual factors when developing smoking cessation programs for young persons who smoke. The study also sheds light on self-conscious emotions towards cessation, such as embarrassment when using cessation services, which are often overlooked in quantitative reviews. Overall, this study has important implications for developing effective smoking cessation interventions and policies that address the complex factors influencing smoking behavior among young persons.

Data on prognostic factors associated with 3-month and 1-year mortality from infective endocarditis

This article describes supplementary tables and figures associated with the research paper entitled “Impact of referral bias on prognostic studies outcomes: insights from a population-based cohort study on infective endocarditis”. The aforementioned paper is a secondary analysis of data from the EI 2008 cohort on infective endocarditis and aimed at characterising referral bias. A total of 497 patients diagnosed with definite infective endocarditis between January 1^st and December 31^st 2008 were included in EI 2008. Data were collected from hospital medical records by trained clinical research assistants. Patients were divided into three groups: admitted to a tertiary hospital (group T), admitted to a non-tertiary hospital and referred secondarily to a tertiary hospital (group NTT) or admitted to a non-tertiary hospital and not referred (group NT). The pooled (NTT+T) group mimicked studies recruiting patients in tertiary hospitals only. Two different starting points were considered for follow up: date of first hospital admission and date of first admission to a tertiary hospital if any (hereinafter referred to as “referral time”). Referral bias is a type of selection bias which can occur due to recruitment of patients in tertiary hospitals only (excluding those who are admitted to non-tertiary hospitals and not referred to tertiary hospitals). This bias may impact the description of patients’ characteristics, survival estimates as well as prognostic factors identification. The six tables presented in this paper illustrate how patients’ selection (population-based sample [pooled (NT+NTT+T) group] versus recruitment in tertiary hospitals only [pooled (NTT+T) group]) might impact Hazards Ratios values for prognostic factors. Crude and adjusted Cox regression analyses were first performed to identify prognostic factors associated with 3-month and 1-year mortality in the whole sample using inclusion as the starting point. Analyses were then performed in the pooled (NTT+T) group first using inclusion as the starting point and finally using referral time as the starting point. Figures 1 to 3 illustrate how HR increase with time for covariates that were considered as time-varying covariates (covariate*time interaction).

[Determinants of time required by medical information technicians for quality control of hospital activity coding, in French medico-administrative system]

BACKGROUND

Since 2008, in France, hospital funding is determined by the nature of activities provided (activity-based funding). Quality control of hospital activity coding is essential to optimize hospital remuneration. There is a need for reliable tools to allocate human resources wisely in order to improve these controls.

METHODS

The main objective of this study was to identify the determinants of time needed by medical information technicians to control hospital activity coding in a Regional Hospital Center. From March 2016 to the beginning of January 2017, medical information technicians reported the time they spent on each quality control, and the time they needed when they had to code the entire stay. Multiple linear regressions were performed to identify the determinants of quality control or coding duration. A split sample validation was used: model was created on one half of the sample and validated on the remaining half.

RESULTS

Among the controls, 5431 were included in the analysis of determinants of control duration (2715 kept aside for model validation). Seven determinants have been identified (stay duration, level of complexity, month of control, type of control, medical information technician, rank of classing information, and major diagnostic category). The correlation coefficient between predicted and real control duration was 0.71 (P<10^-4); 808 stays were included in the analysis of determinants of coding duration (404 kept aside for model validation). Two determinants have been identified. The correlation coefficient, between predicted and real coding duration, was 0.47 (P<10^-3). We performed the same multiple regression, on 2017 activity data, to estimate the weight of each hospital activity pole, regarding quality control of hospital activity coding.

CONCLUSION

We succeeded in modeling time needed for quality control of hospital stays. These results helped to estimate human resources required for quality control of each hospital pole. Nevertheless, the second analysis did not give satisfactory results: we failed in modeling time needed to code hospital stays.